Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

IntroductionLeaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.MethodsTaking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.ResultsThe ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).DiscussionThis study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing

Molecular mechanisms for the adaptive switching between the OAS/RNase L and OASL/RIG-I pathways in birds and mammals:Adaptive exchanging of the OAS/RNase L and OASL/RIG-I pathway

Host cells develop the OAS/RNase L [2′–5′–oligoadenylate synthetase (OAS)/ribonuclease L] system to degrade cellular and viral RNA, and/or the OASL/RIG-I (2′–5′–OAS like/retinoic acid inducible protein I) system to enhance RIG-I-mediated IFN induction, thus providing the first line of defense against viral infection. The 2′–5′–OAS-like (OASL) protein may activate the OAS/RNase L system using its typical OAS-like domain (OLD) or mimic the K63-linked pUb to enhance antiviral activity of the OASL/RIG-I system using its two tandem ubiquitin-like domains (UBLs). We first describe that divergent avian (duck and ostrich) OASL inhibit the replication of a broad range of RNA viruses by activating and magnifying the OAS/RNase L pathway in a UBL-dependent manner. This is in sharp contrast to mammalian enzymatic OASL, which activates and magnifies the OAS/RNase L pathway in a UBL-independent manner, similar to 2′–5′–oligoadenylate synthetase 1 (OAS1). We further show that both avian and mammalian OASL can reversibly exchange to activate and magnify the OAS/RNase L and OASL/RIG-I system by introducing only three key residues, suggesting that ancient OASL possess 2–5A [px5′A(2′p5′A)n; x = 1-3; n ≥ 2] activity and has functionally switched to the OASL/RIG-I pathway recently. Our findings indicate the molecular mechanisms involved in the switching of avian and mammalian OASL molecules to activate and enhance the OAS/RNase L and OASL/RIG-I pathways in response to infection by RNA viruses

Applications of Skyrme energy-density functional to fusion reactions spanning the fusion barriers

The Skyrme energy density functional has been applied to the study of heavy-ion fusion reactions. The barriers for fusion reactions are calculated by the Skyrme energy density functional with proton and neutron density distributions determined by using restricted density variational (RDV) method within the same energy density functional together with semi-classical approach known as the extended semi-classical Thomas-Fermi method. Based on the fusion barrier obtained, we propose a parametrization of the empirical barrier distribution to take into account the multi-dimensional character of real barrier and then apply it to calculate the fusion excitation functions in terms of barrier penetration concept. A large number of measured fusion excitation functions spanning the fusion barriers can be reproduced well. The competition between suppression and enhancement effects on sub-barrier fusion caused by neutron-shell-closure and excess neutron effects is studied.Comment: 28 pages, 13 figures and 2 tables. accepted by Nucl. Phys.

Structural and Functional Analysis of Laninamivir and its Octanoate Prodrug Reveals Group Specific Mechanisms for Influenza NA Inhibition

The 2009 H1N1 influenza pandemic (pH1N1) led to record sales of neuraminidase (NA) inhibitors, which has contributed significantly to the recent increase in oseltamivir-resistant viruses. Therefore, development and careful evaluation of novel NA inhibitors is of great interest. Recently, a highly potent NA inhibitor, laninamivir, has been approved for use in Japan. Laninamivir is effective using a single inhaled dose via its octanoate prodrug (CS-8958) and has been demonstrated to be effective against oseltamivir-resistant NA in vitro. However, effectiveness of laninamivir octanoate prodrug against oseltamivir-resistant influenza infection in adults has not been demonstrated. NA is classified into 2 groups based upon phylogenetic analysis and it is becoming clear that each group has some distinct structural features. Recently, we found that pH1N1 N1 NA (p09N1) is an atypical group 1 NA with some group 2-like features in its active site (lack of a 150-cavity). Furthermore, it has been reported that certain oseltamivir-resistant substitutions in the NA active site are group 1 specific. In order to comprehensively evaluate the effectiveness of laninamivir, we utilized recombinant N5 (typical group 1), p09N1 (atypical group 1) and N2 from the 1957 pandemic H2N2 (p57N2) (typical group 2) to carry out in vitro inhibition assays. We found that laninamivir and its octanoate prodrug display group specific preferences to different influenza NAs and provide the structural basis of their specific action based upon their novel complex crystal structures. Our results indicate that laninamivir and zanamivir are more effective against group 1 NA with a 150-cavity than group 2 NA with no 150-cavity. Furthermore, we have found that the laninamivir octanoate prodrug has a unique binding mode in p09N1 that is different from that of group 2 p57N2, but with some similarities to NA-oseltamivir binding, which provides additional insight into group specific differences of oseltamivir binding and resistance

Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%

State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance Qk and observes the noise covariance Rk , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance Q?,k and observes the noise covariance R?,k. Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use

Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%

Research on State of Power Estimation of Echelon-Use Battery Based on Adaptive Unscented Kalman Filter

An echelon-use lithium-ion battery (EULB) refers to a powered lithium-ion battery used in electric vehicles when the battery capacity is attenuated to less than 80% and greater than 20%. Aiming at the degradation of the performance of the EULB and the unclear initial value of the state of energy (SOE), estimations of the state of power (SOP) of an EULB are not accurate. An SOP estimation method based on an adaptive dual unscented Kalman filter (ADUKF) is proposed. First, the second-order resistor-capacitance symmetry equivalent model (SRCSEM) of the EULB is established. Second, an unscented transformation (UT) is introduced and the battery parameters estimated by the ADUKF: (a) the SOE is estimated based on an adaptive unscented Kalman filtering (AUKF) algorithm, that uses the observation noise equation γk, Rk and the processes noise equation qk, Qk, and (b) the ohmic internal resistance (OIR) and actual capacity (AC) are estimated based on the aforementioned algorithm, which uses the observation noise equation γθ,k, Rθ,k and the process noise equation qθ,k, Qθ,k. Third, the working voltage and OIR are predicted using optimal estimation, and the SOP of the EULB is estimated. MATLAB simulation results show that EULB symmetry capacity decays to 80%, 60%, 40%, and 20% of rated capacity, the proposed algorithm is adaptive regardless of whether the initial SOE value is consistent with the actual value, and the estimation error of the EULB’s SOP is less than 3.28%, showing high accuracy. The results of this study can provide valuable reference for estimating EULB parameters, and help to understand the usage behavior of retired batteries